Adaptive Multi-Layered Non-Terrestrial Network for Deep Learning-Enhanced Global Connectivity

12 pages•Published: October 17, 2024

Osei Wusu Brempong Jnr, Junaid Hussain Muzamal, Okechukwu Clement Agomuo and Zohaib Khan

Abstract

This paper presents the Adaptive Deep Learning-Enhanced Non-Terrestrial Network (ADL-NTN), an innovative framework that combines satellites, High Altitude Platform Stations (HAPS), and Unmanned Aerial Vehicles (UAVs). By integrating Free-Space Op- tical (FSO) and Radio Frequency (RF) communications optimised for different altitudes, this architecture aims to improve connectivity in remote and disaster-affected regions. The ADL-NTN employs deep learning algorithms for dynamic power distribution and link opti- misation, significantly enhancing the network’s robustness and adaptability to environmen- tal conditions and varying demands. Simulations conducted in OMNeT++ demonstrate substantial improvements, with throughput increasing by up to 37% and latency decreas- ing by 42%, surpassing traditional NTN systems. The ADL-NTN architecture exhibits exceptional resilience, ensuring high-quality service delivery under diverse conditions. This research sets the stage for integrating future communication technologies and expanding the framework for global implementation. The ADL-NTN offers groundbreaking solutions for enhancing rural connectivity and providing rapid disaster response, significantly con- tributing to global digital inclusion

Keyphrases: amlt ntn, deep learning, dynamic power allocation, free space optical (fso) communication, high altitude platform stations (haps), machine learning, non terrestrial network, radio frequency (rf) communication, real time optimization algorithms, rural connectivity, satellite communication, unmanned aerial vehicles (uavs)

In: Lindsay Quarrie (editor). Proceedings of 2024 Concurrent Processes Architectures and Embedded Systems Hybrid Virtual Conference, vol 20, pages 47-58.

Links:	https://easychair.org/publications/paper/VRd4
	https://doi.org/10.29007/l5lc

BibTeX entry

@inproceedings{COPA2024:Adaptive_Multi_Layered_Non,
  author    = {Osei Wusu Brempong Jnr and Junaid Hussain Muzamal and Okechukwu Clement Agomuo and Zohaib Khan},
  title     = {Adaptive Multi-Layered Non-Terrestrial Network for Deep Learning-Enhanced Global Connectivity},
  booktitle = {Proceedings of 2024 Concurrent Processes Architectures and Embedded Systems Hybrid Virtual Conference},
  editor    = {Lindsay Quarrie},
  series    = {Kalpa Publications in Computing},
  volume    = {20},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/VRd4},
  doi       = {10.29007/l5lc},
  pages     = {47-58},
  year      = {2024}}

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